Exploration and development of hydrocarbon reservoirs may be efficiently done with the help of seismic data, which must be properly processed in order to allow interpretation of subsurface features. In practice, seismic data is often contaminated by noise which may be coherent or incoherent (e.g. random) in nature. In addition, the noise level may vary both spatially and temporally.
Conventional noise suppression methods often have difficulty estimating and removing spatially and temporally varying noise. Conventional methods may try to normalize the amplitudes across the seismic data prior to the attenuation step, often using an algorithm like Automatic Gain Control (AGC). This may lead to erroneous suppression of signal in areas with strong signal and weak noise.
Efficient and effective methods for estimating and attenuating spatially and temporally varying noise in seismic data are needed to improve the final seismic image and allow proper interpretation of the subsurface features.